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      <title>并发集合的实现与优化 - 交互式测验</title>
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      <div class="main-wrapper">
          <div class="quiz-container">
            <div class="quiz-header">
              <h1>并发集合的实现与优化 - 交互式测验</h1>
              <p>检验你对文档内容的理解程度</p>
            </div>
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  {
    "question": "根据文档，`ConcurrentHashMap` (特指文档中描述的基于Segment的版本) 通过哪种核心技术来提升并发性能？",
    "options": [
      "写时复制 (Copy-On-Write)",
      "分段锁 (Segment Lock)",
      "单一全局锁 (Single Global Lock)",
      "CAS无锁操作 (CAS Lock-free Operation)"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档指出，`ConcurrentHashMap` 将其内部数据结构划分为多个段（Segment），每个段都拥有独立的锁。这使得多个线程可以同时在不同的段上进行操作，而不会相互阻塞，从而大大提高了并发性能。",
    "source": "文档中「2. `ConcurrentHashMap` 的实现」部分的「分段锁与分段映射」",
    "hint": "文档提到：'它通过将数据划分成多个段（Segment）来实现并发操作中的锁分离。'"
  },
  {
    "question": "`CopyOnWriteArrayList` 在提供线程安全的同时，存在一个显著的缺点，根据文档描述，这个缺点是什么？",
    "options": [
      "读取操作性能低下",
      "不支持并发读取",
      "频繁的写操作会导致较高的内存消耗和性能开销",
      "实现复杂，容易引入BUG"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档明确指出，`CopyOnWriteArrayList` 采用“写时复制”策略，每次修改（如add操作）都会创建一个新的底层数组副本。因此，在写操作频繁的场景下，会产生大量的内存分配和复制开销，导致性能下降和内存消耗增高。",
    "source": "文档中「3. `CopyOnWriteArrayList` 的实现」部分的「写时复制与内存消耗」",
    "hint": "文档提示：'...由于每次修改都需要复制数组，因此内存消耗较高。'"
  },
  {
    "question": "根据文档对 `BlockingQueue` 的描述，当一个线程尝试向一个已满的 `ArrayBlockingQueue` 中执行 `put` 操作时，会发生什么？",
    "options": [
      "操作将立即失败并抛出异常",
      "该线程将被阻塞，直到队列中有可用空间",
      "新元素会覆盖队列中最早的元素",
      "该操作会成功，但返回一个false值"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档中解释了 `BlockingQueue` 的核心特性：当队列已满时，尝试放入元素的 `put` 操作将会阻塞，直到其他线程从队列中取走元素，释放出空间。这是其作为“阻塞队列”的关键行为。",
    "source": "文档中「4. `BlockingQueue` 的实现」部分的「阻塞队列设计」",
    "hint": "文档中的代码示例展示了：'while (count == items.length) notFull.await(); // 当队列已满时，阻塞等待'"
  },
  {
    "question": "与使用 `Collections.synchronizedMap` 这样的传统同步集合相比，`ConcurrentHashMap` 等并发集合在设计上的主要优势是什么？",
    "options": [
      "通过细粒度锁或无锁技术在保证线程安全的同时，提供了更好的并发性能",
      "占用的内存空间更小",
      "API接口更简单易用",
      "完全消除了死锁的可能性"
    ],
    "correctAnswerIndex": 0,
    "explanation": "文档的核心思想是，传统同步集合通常在整个集合上加一个全局锁，导致并发度低。而并发集合通过分段锁、CAS等技术实现了更细粒度的控制，允许多个线程在一定程度上并行操作，从而在保证线程安全的前提下，极大地提升了性能。",
    "source": "文档中「1. 并发集合的设计原则」部分的「线程安全与性能的平衡」",
    "hint": "文档提到：'传统的同步集合类通过在整个集合操作时加锁来保证线程安全性，而并发集合通过细粒度锁...来优化性能。'"
  },
  {
    "question": "根据 `CopyOnWriteArrayList` 的“写时复制”特性，它最适合应用于以下哪种业务场景？",
    "options": [
      "数据需要频繁修改和更新的场景",
      "写操作性能要求极高的场景",
      "读操作远多于写操作的场景",
      "需要严格控制内存占用的场景"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档解释说，“写时复制”技术的核心优势在于读取操作无需加锁，因此性能极高。但写操作成本（复制数组）很高。因此，它最适合“读多写少”的场景，例如配置信息、监听器列表等。",
    "source": "文档中「3. `CopyOnWriteArrayList` 的实现」部分的「写时复制技术」",
    "hint": "文档中描述这种技术 '在读取操作非常频繁的场景下非常有用'。"
  },
  {
    "question": "在文档展示的 `ArrayBlockingQueue` 实现代码中，用于实现线程阻塞和唤醒的关键组件是什么？",
    "options": [
      "`synchronized` 关键字和 `wait()/notify()`",
      "`ReentrantLock` 和 `Condition` 条件变量",
      "`AtomicInteger` 和自旋",
      "`Semaphore` 和 `Mutex`"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档中的代码示例清晰地展示了 `ArrayBlockingQueue` 内部使用了一个 `ReentrantLock` 来保证操作的原子性，并配合两个 `Condition` 对象 (`notFull` 和 `notEmpty`) 来精确地控制生产者线程（当队列满时）和消费者线程（当队列空时）的等待和唤醒。",
    "source": "文档中「4. `BlockingQueue` 的实现」部分的「锁与条件变量」",
    "hint": "文档摘要中提到：'使用 ReentrantLock 锁和 Condition 条件变量来控制 put 和 take 操作的阻塞与唤醒。'"
  },
  {
    "question": "当一个线程对 `ConcurrentHashMap` 进行写入操作时，其加锁的范围是怎样的？",
    "options": [
      "锁定整个Map对象，其他所有线程都无法读写",
      "只锁定数据所在的特定Segment，不影响其他Segment的操作",
      "只锁定当前操作的键值对(Entry)",
      "不使用任何锁，完全依赖CAS操作"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档强调了`ConcurrentHashMap`的分段锁机制。锁的作用范围不是整个Map，而是数据所在的那个Segment。这意味着如果两个线程操作的数据位于不同的Segment，它们就可以完全并发执行，互不影响，这是其高性能的关键。",
    "source": "文档中「2. `ConcurrentHashMap` 的实现」部分的「分段锁与分段映射」",
    "hint": "文档提到：'这个锁只作用于它所管理的部分数据，因此并发线程可以在不互相影响的情况下访问不同的段。'"
  }
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            const wrongCount = answeredCount - correctCount;
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                '<p>' + correctCount + ' / ' + totalQuestions + '</p>' +
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                '<div class="score-card">' +
                '<h3>正确率</h3>' +
                '<p>' + accuracy + '%</p>' +
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                '<div class="score-card">' +
                '<h3>错误率</h3>' +
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                '<div class="score-card score-details">' +
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                '<tr><td>错误</td><td>' + wrongCount + '</td></tr>' +
                '<tr><td>未答</td><td>' + skippedCount + '</td></tr>' +
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            document.querySelector('.quiz-body').style.display = 'block';
            document.getElementById('quiz-footer').style.display = 'flex';
            
            currentQuestionIndex = 0;
            loadQuestion();
            updateNavigationButtons(); // Ensure nav buttons are correctly displayed
        }

        // Event Delegation for options
        optionsListEl.addEventListener('click', (event) => {
            if (event.target && event.target.matches('li.option-item')) {
                // Check if the question has already been answered
                if (userAnswers[currentQuestionIndex] === null) {
                    const selectedIndex = parseInt(event.target.dataset.index, 10);
                    selectOption(selectedIndex);
                }
            }
        });

        prevBtn.addEventListener('click', goToPreviousQuestion);
        nextBtn.addEventListener('click', goToNextQuestion);
        hintBtn.addEventListener('click', showHint);
        
        // 初始加载
        loadQuestion();
      </script>
    </body>
    </html>
  